code: def from_rgb(
      input_tensor: tf.Tensor,
      use_fan_in_scaled_kernel: bool,
      num_channels: int,
      kernel_initializer: _KerasInitializer,
      relu_leakiness: float,
      name: str = "from_rgb",
  ) -> tf.Tensor:
      """Converts a rgb input to a feature map.
  
      Args:
        input_tensor: The input feature map.
        use_fan_in_scaled_kernel: If a fan in scaled kernel should be used.
        num_channels: The number of output channels.
        kernel_initializer: The kernel initializer to use.
        relu_leakiness: The leakiness of the ReLU.
        name: The name of the block.
  
      Returns:
        The feature map.
      """
      with tf.name_scope(name):
          output = create_conv_layer(
              use_fan_in_scaled_kernel=use_fan_in_scaled_kernel,
              filters=num_channels,
              kernel_size=1,
              strides=1,
              kernel_initializer=kernel_initializer,
              padding="same",
          )(input_tensor)
          return tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(output)
  
file_path: graphics/tensorflow_graphics/projects/gan/architectures_progressive_gan.py
imports: import math
  from typing import Callable, Optional, Sequence, Union
  import tensorflow
  import tensorflow_addons.layers.normalizations
  from tensorflow_graphics.projects.gan import keras_layers
label_desc: Use image node_id 4 for calling a global function with example usage: from_rgb(input_tensor, use_fan_in_scaled_kernel, num_channels, kernel_initializer, relu_leakiness, name) and returns: tf